Spike timing dependent plasticity finds the start of repeating patterns

Masquelier et al found evidence in support of the belief that spike timing dependant plasticity (STDP) makes the post-synaptic neuron respond more quickly. In their model multiple afferents converge upon a single post-synaptic neuron. Interestingly their work does not demand that a pattern to be learnt be present in all spike volleys. Distractor spike volleys are not only present in between presentations of the learned pattern, but in addition a constant population firing rate is effective throughout all the stimuli to ensure that the what network learns is not a side effect of conditions other that the coincidence of the pattern to be learned being repeated. Confirming earlier conclusions STDP first of all leads to an overall weakening of synapses, but by reinforcing the synaptic connections with the afferents that took part in firing the neuron when the pattern to be learned was present it then increases the probability that the neuron fires again next time the pattern is presented. After only 70 pattern presentations the neuron stops discharging outside of the pattern presentation. Though at first chance determines which part of the pattern the neuron becomes selective to, by reinforcing the connections to pre-synaptic neurons that fired slightly before the post-synaptic neuron the post-synaptic neuron learns to discharge earlier on presentation of the desired stimulus.

Masquelier et al have extended their model to make it respond to multiple patterns by using multiple post-synaptic neurons with inhibitory connections between them. In this case, the first neuron to fire inhibit others so it only one of the post-synaptic neurons to respond to each stimuli. However, because of the simplicity of this feed forward model and because additive STDP creates a bimodal weight distribution (see this post) distributed around 0 and MAX, in this case MAX being equal to 1, afferents are effectively turned on or off. One can only conclude that STDP is just becoming selective to particular inputs that happen to correspond to part of the stimulus to be learned that are good at identifying the desired stimulus and not the distractor. Network structure only is what is providing the computational power here. Further interesting work would proceed by studying more complex structures than simple feed-forward mechanisms by introducing reciprocal connections. I shall report on these later. For now, off to the pub 🙂

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